1. Technical Field
The disclosure relates to a method and a system for lane departure warning.
2. Related Art
The research and development of systems for lane departure warning mainly include two projects, namely, “lane line detection” and “lane departure detection”. Although the research has a history of more than 10 years with many famous scholars devoted to the research in this field, the research results still need to be improved.
Lane line detection algorithms at the present stage include edge detection algorithms and line detection algorithms, and are mainly applied in detecting manual lane markers. Edge detection is one of common techniques for detecting lane markers.
FIG. 1(a1) to FIG. 1(b2) show examples of conventional edge detection. FIG. 1(a1) and FIG. 1(a2) show original images; and FIG. 1(b1) and FIG. 1(b2) respectively show results obtained by performing edge detection on the original images in FIG. 1(a1) and FIG. 1(a2). The brightness of pixels represents the intensity of an edge (the darker the color, the higher the intensity of the edge). Therefore, in actual application, a threshold needs to be set to classify pixels into edge pixels and non-edge pixels to facilitate subsequent steps of a lane detection algorithm, but the setting of such a threshold is very difficult.
The conventional edge detection algorithms need to be improved in many parts. First, although edge pixels can be detected using the edge detection method, the detected edge is just the edge of a lane marker, thereby causing a result of a hollow line (that is, the center of the lane marker is judged as non-edge pixels); therefore, some additional steps are required to avoid such result. Second, in the same image, some lane markers with brightness gradient varying sharply can be easily detected, while those lane markers with brightness gradient varying non-obviously cannot be detected using the conventional edge detection algorithms. Third, in order to process “short lane lines”, the conventional edge detection algorithm needs additional pre-processing steps to ensure the ability of detecting “short lane lines”. For example, the image overlapping technology can be used to extend “short lane lines” by overlapping a plurality of images. In this way, erroneous detection can be reduced.
FIG. 2(a) to FIG. 2(e) show an illustrative example of a conventional line detection method. FIG. 2(a) shows a point (x,y) in two-dimensional space on which coordination conversion is performed according to the following formula (1):w=x cos(φ)+y sin(φ)  (1)
In the coordinate conversion formula, since x and y are known numbers, for each different variable φ (from 0° to 180°), the corresponding value of w can be calculated, thus obtaining the cumulative matrix in FIG. 2(b). Therefore, each point in xy space can be converted into a curve in wφ space, and the number of curves with intersection points can be represented as the number of edge points of straight lines in xy coordinates. FIG. 2(c) shows an original image, FIG. 2(d) shows the result of implementing edge detection on FIG. 2(c), and FIG. 2(e) shows the result of a cumulative matrix obtained after implementing transform on each edge point in FIG. 2(d).
It can be clearly found from FIG. 2(e) that there are five bright points (points with the most curves passing through), thereby judging that there are five straight lines in the picture. Although the number of straight lines in the image can be known, the region corresponding to each straight line cannot be further known. That is because in actual environment, the conventional line detection method can provide information on a straight line, but cannot further provide whether the straight line is a “non-lane line”.
A current lane departure algorithm needs to analyze a plurality of continuous frames, and accordingly find a displacement direction of a lane line, so as to judge whether a vehicle departs. By analyzing the variation of a lane line and left and right boundaries, a moving direction of the vehicle can be judged.
Since the existing lane departure algorithm needs to analyze a plurality of continuous frames to judge the moving direction of the vehicle, a certain time delay exists before the result of lane departure detection is obtained, which is also a problem in requirements for real time.
To sum up, since the conventional lane line detection algorithm cannot predict, in lane line detection, the degree of color difference (gradient) between the lane line and the road, all types of lane lines cannot be detected using parameters preset in the algorithm. In addition, to strengthen characteristics of the lane line, a plurality of continuous frames always needs to be overlapped to lengthen the lane line. Finally, since the used line detection algorithm cannot provide whether the straight line is a lane line or a non-lane line, in the conventional lane departure system, a manually set frame is required, and possible lane line regions need to be marked, so as to filter out non-road lines. In lane departure detection, the conventional lane departure system needs to analyze the variation of continuous frames to judge whether the vehicle departs. In this way, the system cannot notify the driver of information on lane departure in real time.